Problem solved

Openllmetry solves the challenge of monitoring and debugging LLM-based applications by providing standardized, vendor-neutral observability. It captures detailed telemetry data such as prompts, completions, token counts, latency, and model metadata, enabling teams to understand performance, cost, and reliability of their AI services.

Use cases

• Tracing LLM calls across microservices to identify bottlenecks
• Monitoring token usage and costs per request
• Debugging latency issues in AI applications
• Evaluating model performance and quality over time
• Integrating LLM observability with existing OpenTelemetry pipelines
• Tracking errors and anomalies in generative AI responses

Audience

• Developers
• ML Engineers
• Data Scientists

Pros

• Open-source and vendor-neutral
• Based on OpenTelemetry standard
• Automatic instrumentation for many LLM frameworks
• Supports Python and TypeScript
• Integration with existing observability stacks

Cons

• Requires setting up an OpenTelemetry backend for full value
• May not cover all niche LLM frameworks
• Documentation can lag behind features
• Advanced features may be limited compared to commercial tools